Speaker
Description
Stable operation of detectors, beams, and targets is crucial for reducing systematic errors and achieving high-precision measurements in accelerator-based experiments. Historically, this stability was achieved through extensive post-acquisition calibration and systematic data studies, as not all operational parameters could be precisely controlled in real time. However, recent advances in AI/ML are transforming this landscape by enabling dynamic, in-situ adjustments of equipment parameters during data acquisition.
At Jefferson Lab, a two-phase AI/ML program has been initiated to address these challenges. The first phase successfully deployed a system that dynamically adjusts the high voltage of a gaseous drift chamber detector to stabilize its gain—a solution that has been in production use for over a year. Building on this success, the second phase is now underway. This effort focuses on automating the continuous operation of a linearly polarized photon beam and the extraction of the polarization of a cryotarget used in fixed-target nuclear physics experiments.
A key innovation across both phases is the integration of uncertainty quantification within the AI/ML models, which provides not only accurate predictions but also confidence estimates that are critical for near real-time decision making and control. This presentation will highlight achievements from the drift chamber production system and discuss the current progress on automating beam and target operations, outlining the methodologies, challenges, and safety protocols that ensure robust performance in a dynamic experimental environment.
References
https://doi.org/10.1051/epjconf/202429502003
Significance
This talk will report on a program that includes a project that has been in operation for >1yr and a new pair of ongoing projects that are a natural progression in the AI/ML control space. It will tie in what was learned during the first project and how it is being applied in the next phase in the form of best practices.
| Experiment context, if any | GlueX and CLAS12 |
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